Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology.

Introduction:: To provide a complementary solution to the need for simultaneous whole-cell metabolic and protein analysis in situ, we propose Single Cell SPAtially resolved METabolic (scSpaMet) framework for profiling immune cells and cancer cells in human tissues at the single-cell level by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline (Figure 1. a). scSpatMet combines previously developed 3D-SMF¹ submicron resolution metabolites imaging framework with multiplex IMC² proteomic imaging for cell-type characterization in the same tissue sample. scSpatMet enables the correlation of more than 200 metabolic markers and 25 protein markers in individual cells within native tissues. Moreover, scSpaMet introduces additional analysis capabilities for joint metabolomic and proteomic single-cell data.



Materials and Methods:: The human lung adenocarcinoma tissues were obtained from an FFPE tonsil block (ID BS04081a, BioMax). The human tonsil tissue sections were from TissueArray.com under the IDs HuFPT161. Tissues were labeled by a 20-plex isotope-barcoded antibody library one-time to enhance the contrast of specific immune and cancer cell types. Labeled samples were then analyzed by TOF-SIMS using the 3D-SMF protocol for spatial metabolites imaging. Then, IMC was used for multiplexed protein imaging for unsupervised single-cell phenotyping. Spatial protein and metabolites imaging modalities were registered using single-cell nuclei features with a cross-correlation metric.


Results, Conclusions, and Discussions:: Immune and cancer cells exhibited spatially distinct lipidomic fragment distributions in lung cancer tissue. Immune, cancer and other cell types were defined using multiplex protein maps. 3D-SMF¹ profiling in human tissues shows heterogeneity and details in the tissue microenvironment. Cell phenotypes defined from multiplex protein markers exhibited lipid metabolism depletion and enhancement. ScSpaMet identified metabolite variation between cells in the tumor and stromal regions (Figure 1b), cell type-specific local metabolic competition, metabolic trajectories, and patient-level molecule variation in lung cancer tissues (Figure 1c). Similarly, scSpaMet quantified metabolic changes around B-cell follicles (Figure 1c) in human tonsil tissues by looking at B-cells, T-cells, and FDCs inside germinal center LZ/DZ regions, cell type-specific local metabolic competition inside of germinal center (Figure 1e), and metabolomic changes along B-cell differentiation trajectories (Figure 1f).


scSpaMet profiling technology characterizes the cellular and molecular diversity in the tumor microenvironment and immune microenvironment at the single-cell level. By leveraging 3D-SMF¹ for metabolomic compound detection for up to 200 markers using a TOF-SIMS device and IMC² multiplex protein imaging, the scSpaMet pipeline localized individual cell types and mapped hundreds of metabolic markers in each cell type within the tissue microenvironment. scSpaMet allows high-resolution joint protein and metabolite profiling at the single-cell level in the same tissue. Using protein markers, single cells are annotated and their metabolic variation is quantified. Combining cell type spatial information and metabolic profile, a local cell metabolite competition framework is proposed. Moreover, metabolic reprogramming along cell differentiation trajectories can be retraced and projected spatially. With the advancement of spatial mass spectrometry imaging resolution, molecule annotation capabilities, and efficiency, scSpaMet paves the way to systematic single-cell metabolite and protein profiling in their tissue environment.




Acknowledgements (Optional): :
AFC holds a Career Award at the Scientific Interface from Burroughs Wellcome Fund and a Bernie-Marcus Early-Career Professorship. A. F. C. was supported by start-up funds from the Georgia Institute of Technology and Emory University. This work was performed in part at the Materials Characterization Facility (MCF) at Georgia Tech. The MCF is jointly supported by the GT Institute for Materials (IMat) and the Institute for Electronics and Nanotechnology (IEN), which is a member of the National Nanotechnology Coordinated Infrastructure supported by the National Science Foundation (Grant ECCS-1542174). This work was performed in part at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (Grant ECCS-2025462). Part of the work was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility, and using instrumentation within ORNL's Materials Characterization Core provided by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. IEN also provided financial support in the form of a Facility Seed Grant. Supported in part by Winship Cancer Institute # IRG-21-137-07 -IRG from the American Cancer Society and American Lung Association Innovation Award.




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